library(pacman)
## Warning: package 'pacman' was built under R version 4.3.3
p_load(tidyverse)
library(ggplot2)
library(plotly)
## Warning: package 'plotly' was built under R version 4.3.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout

A Simple World Map

map<-map_data("world")
ggplot(map,
       aes(x=long,y=lat,group=group))+
  geom_polygon(fill="lightblue",colour="white")+
  theme_void()

A Map for Specific Regions

North_Asia<-c("China","Japan","Mongolia","North Korea","South Korea","Taiwan")
North_Asia_map<-map_data("world",region=North_Asia)

#Compute the centroid as the mean longitude and latitude
region.data<-North_Asia_map %>%
  group_by(region) %>%
  summarize(long=mean(long), lat=mean(lat)) %>%
  arrange()

#Ready to plot the map
ggplot(North_Asia_map,
       aes(x=long,y=lat))+
  geom_polygon(aes(group=group,fill=region))+
  geom_text(data=region.data,aes(label=region),
            size=5, hjust=0.5, col="#808080",
            fontface="bold")+
  scale_fill_viridis_d()+
  theme_void()+
  theme(legend.position="none")

A Choropleth Map

drinks<-read_csv("G:/My Drive/Spring 2025/MTH 209/In Class Labs/drinks.csv")
## Rows: 193 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (4): beer_servings, spirit_servings, wine_servings, total_litres_of_pure...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
drinks_map<-drinks %>%
  left_join(map,by=c("country"="region"))
ggplot(drinks_map,
       aes(long,lat,group=group))+
  geom_polygon(aes(fill=total_litres_of_pure_alcohol),
               color="white")+
  scale_fill_viridis_c(option="C")+
  labs(fil="Total Litres of Pure Alcohol")+
  theme_void()+
  theme(legend.position="bottom")

ggplot(drinks,
       aes(map_id=country))+
  geom_map(aes(fill=total_litres_of_pure_alcohol),
           map=map,color="white")+
  expand_limits(x=map$long, y=map$lat)+
  labs(fill="Total Litres of Pure Alcohol")+
  theme_void()+
  theme(legend.position="bottom")

A US State-Level Map

US_map<-map_data("state")

# compute the centroid as the mean long and lat
state_data<-US_map %>%
  filter(region  != "district of columbia") %>%
  group_by(region) %>%
  summarize(long=mean(long),lat=mean(lat)) %>%
  arrange(region)

state_data$region.abb<-state.abb[-c(2,11)] # drop Alaska and Hawaii

p<-ggplot(US_map, aes(x=long, y=lat))+
  geom_polygon(aes(group=group,fill=region),
color="white")+
  geom_text(data=state_data,
            aes(label=state_data$region.abb),
            fontface="bold")+
  theme_void()+
  theme(leged.position="none")
  
p
## Warning: Use of `state_data$region.abb` is discouraged.
## ℹ Use `region.abb` instead.
## Warning in plot_theme(plot): The `leged.position` theme element is not defined
## in the element hierarchy.

A Dynamic Map using plotly - 1

library(plotly)
ggplotly(p)
## Warning: Use of `state_data$region.abb` is discouraged.
## ℹ Use `region.abb` instead.
## Warning in ggfun("plot_theme")(plot): The `leged.position` theme element is not
## defined in the element hierarchy.

A Dynamic Map using plotly - 2

US_map<-map_data("state")

# compute the centroid as the mean long and lat
state_data<-US_map %>%
  filter(region != "district of columbia") %>%
  group_by(region) %>%
  summarize(long=mean(long),lat=mean(lat)) %>%
  arrange(region)

state_data$region.abb<-state.abb[-c(2,11)] # drop Alaska and Hawaii

crimes<- data.frame(region=rownames(USArrests), USArrests) %>%
  filter(region!=c("Alaska","Hawaii"))

crimes$region<-tolower(crimes$region)

crimes_map<- crimes %>%
  left_join(US_map, by="region")

g1<-ggplot(crimes_map, aes(x=long,y=lat))+
  geom_polygon(aes(group=group, fill=Murder,
                   text=paste0(region, ":\n",Murder, " murder arrests per 100,000"),
                   color="white"))+
  geom_text(data=state_data,
            aes(label=region.abb),fontface="bold",size=3)+
  scale_fill_viridis_c(option="C")+
  theme_void()
## Warning in geom_polygon(aes(group = group, fill = Murder, text = paste0(region,
## : Ignoring unknown aesthetics: text
ggplotly(g1,tooltip="text")

A County-Level Map

covid<-read_csv("./COVID19.csv")
## Rows: 1175665 Columns: 30
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (4): id, administrative_area_level_1, administrative_area_level_2, adm...
## dbl  (25): confirmed, deaths, people_vaccinated, people_fully_vaccinated, sc...
## date  (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
ohio<-covid %>%
  filter(administrative_area_level_2=="Ohio",
         date=="2021-12-31")

ohio<-ohio%>%
  rename(county=administrative_area_level_3)

ohio_county<-map_data("county",region="ohio")

ohio_county$subregion<-str_to_title(ohio_county$subregion)

ohio_map<- ohio%>%
  left_join(ohio_county,
            by=c("county"="subregion"))

g2<-ggplot(ohio_map,
           aes(x=long,y=lat))+
  geom_polygon(aes(group=group,fill=deaths,
                   text=paste0("County: ", county, "\n",
                               "Total Deaths: ", deaths)))+
  geom_text(data=ohio,
            aes(x=longitude, y=latitude, label=county),
            color="white", fontface="bold")+
  scale_fill_viridis_c(option="H")+
  theme_minimal()+
  theme_void()
## Warning in geom_polygon(aes(group = group, fill = deaths, text =
## paste0("County: ", : Ignoring unknown aesthetics: text
font<- list(
  family="Arial",
  size=15,
  color="white"
)

label<- list(
  bgcolor="#232F34",
  bordercolor="transparent",
  font=font
)

ggplotly(g2, tooltop="text",
         width=850,height=800) %>%
           style(hoverlabel=label) %>% 
           layout(font=font)